{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tbno/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/tbno/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/tbno/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/tbno/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/tbno/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/tbno/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/home/tbno/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/tbno/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/tbno/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/tbno/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/tbno/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/tbno/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "tf.logging.set_verbosity(tf.logging.INFO) \n",
    "#作用：将 TensorFlow 日志信息输出到屏幕"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-2-b0ffac263758>:1: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "WARNING:tensorflow:From /home/tbno/.local/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please write your own downloading logic.\n",
      "WARNING:tensorflow:From /home/tbno/.local/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting ./train-images-idx3-ubyte.gz\n",
      "WARNING:tensorflow:From /home/tbno/.local/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting ./train-labels-idx1-ubyte.gz\n",
      "Extracting ./t10k-images-idx3-ubyte.gz\n",
      "Extracting ./t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From /home/tbno/.local/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "(55000, 784)\n",
      "(55000,)\n",
      "(5000, 784)\n",
      "(5000,)\n",
      "(10000, 784)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets(\"./\")\n",
    "\n",
    "print(mnist.train.images.shape)\n",
    "print(mnist.train.labels.shape)\n",
    "\n",
    "print(mnist.validation.images.shape)\n",
    "print(mnist.validation.labels.shape)\n",
    "\n",
    "print(mnist.test.images.shape)\n",
    "print(mnist.test.labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 2 1 0 4 1 4 9 5 9]\n"
     ]
    }
   ],
   "source": [
    "print(mnist.test.labels[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "for idx in range(16):\n",
    "    plt.subplot(4,4,idx+1)\n",
    "    plt.axis('off')\n",
    "    plt.title('[{}]'.format(mnist.train.labels[idx]))\n",
    "    plt.imshow(mnist.train.images[idx].reshape((28,28)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.placeholder(\"float\", [None, 784])\n",
    "y = tf.placeholder('int64', [None])\n",
    "learning_rate = tf.placeholder('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize(shape,stddev=0.1):\n",
    "    return tf.truncated_normal(shape, stddev=0.1)\n",
    "\n",
    "L1_units_count = 100\n",
    "\n",
    "W_1 = tf.Variable(initialize([784,L1_units_count]))\n",
    "b_1 = tf.Variable(initialize([L1_units_count]))\n",
    "logits_1 = tf.matmul(x,W_1)+b_1\n",
    "output_1 = tf.nn.relu(logits_1)\n",
    "\n",
    "L2_units_count=10\n",
    "W_2 = tf.Variable(initialize([L1_units_count,L2_units_count]))\n",
    "b_2 = tf.Variable(initialize([L2_units_count]))\n",
    "logits_2 = tf.matmul(output_1, W_2)+b_2\n",
    "\n",
    "logits = logits_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cross_entropy_loss = tf.reduce_mean(\n",
    "    tf.nn.sparse_softmax_cross_entropy_with_logits(\n",
    "        logits=logits,\n",
    "        labels=y\n",
    "    ))\n",
    "\n",
    "optimizer = tf.train.GradientDescentOptimizer(\n",
    "    learning_rate = learning_rate).minimize(cross_entropy_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = tf.nn.softmax(logits)\n",
    "correct_pred = tf.equal(tf.argmax(pred,1),y)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "training_step = 1000\n",
    "\n",
    "saver = tf.train.Saver() #用于保存或恢复训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 100 training steps,the loss is 0.354704, the validation accuracy is 0.88\n",
      "after 200 training steps,the loss is 0.3039, the validation accuracy is 0.911\n",
      "after 300 training steps,the loss is 0.253164, the validation accuracy is 0.9042\n",
      "after 400 training steps,the loss is 0.131272, the validation accuracy is 0.932\n",
      "after 500 training steps,the loss is 0.244795, the validation accuracy is 0.9416\n",
      "after 600 training steps,the loss is 0.0606853, the validation accuracy is 0.9412\n",
      "WARNING:tensorflow:From /home/tbno/.local/lib/python3.6/site-packages/tensorflow/python/training/saver.py:960: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to delete files with this prefix.\n",
      "after 700 training steps,the loss is 0.304191, the validation accuracy is 0.9462\n",
      "after 800 training steps,the loss is 0.128301, the validation accuracy is 0.9442\n",
      "after 900 training steps,the loss is 0.421686, the validation accuracy is 0.9502\n",
      "the training is finish\n",
      "the test accuracy is: 0.9495\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "    #定义验证集和测试集\n",
    "    validate_data = {\n",
    "        x:mnist.validation.images,\n",
    "        y:mnist.validation.labels,\n",
    "    }\n",
    "    test_data = {x:mnist.test.images,y:mnist.test.labels}\n",
    "    \n",
    "    for i in range(training_step):\n",
    "        xs, ys = mnist.train.next_batch(batch_size)\n",
    "        _, loss = sess.run(\n",
    "            [optimizer,cross_entropy_loss],\n",
    "            feed_dict={\n",
    "                x:xs,\n",
    "                y:ys,\n",
    "                learning_rate:0.3\n",
    "            })\n",
    "        \n",
    "        #每100次训练打印一次损失与验证准确率        \n",
    "        if i > 0 and i % 100 ==0:\n",
    "            validate_accuracy = sess.run(accuracy, feed_dict = validate_data)\n",
    "            print(\n",
    "                \"after %d training steps,the loss is %g, the validation accuracy is %g\"%(i, loss, validate_accuracy))\n",
    "            saver.save(sess, \"./model.ckpt\", global_step = i)\n",
    "            \n",
    "    print(\"the training is finish\")\n",
    "    #最终的测试准确率\n",
    "    acc = sess.run(accuracy, feed_dict = test_data)\n",
    "    print(\"the test accuracy is:\", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/tbno/.local/lib/python3.6/site-packages/tensorflow/python/training/saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to check for files with this prefix.\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt-900\n",
      "1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    ckpt = tf.train.get_checkpoint_state(\"./\")\n",
    "    if ckpt and ckpt.model_checkpoint_path:\n",
    "        saver.restore(sess,ckpt.model_checkpoint_path)\n",
    "        final_pred, acc = sess.run(\n",
    "            [pred, accuracy],\n",
    "            feed_dict = {\n",
    "                x:mnist.test.images[:16],\n",
    "                y:mnist.test.labels[:16]\n",
    "            })\n",
    "        orders = np.argsort(final_pred)\n",
    "        plt.figure(figsize=(8,8))\n",
    "        print(acc)\n",
    "        for idx in range(16):\n",
    "            order = orders[idx,:][-1]\n",
    "            prob = final_pred[idx, :][order]\n",
    "            plt.subplot(4,4,idx+1)\n",
    "            plt.axis(\"off\")\n",
    "            plt.title(\"{}:[{}]-[{:.1f}%]\".format(mnist.test.labels[idx],\n",
    "                                                order, prob*100))\n",
    "            plt.imshow(mnist.test.images[idx].reshape((28,28)))\n",
    "            \n",
    "        else:\n",
    "            pass"
   ]
  },
  {
   "attachments": {
    "%EF%BC%92%EF%BC%92%EF%BC%92.png": {
     "image/png": 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4gSjfj/Zj2SyvW03dztTRrm0IjvNW1AuMqJGxbE4ir34TlSdWo0bGft6JfoEu7LKsB1axS/UOR9YYg/8QaAuZspVJ1P7iOPkp4ZcrEAgE4SA8XgLBY4D98z3Y4tKJ7xBdrlryXjFheiEbm9+1o39pPZR/EPh9LKFbYEQNIDdwsgoMf5Xo+x3VgEQXsp09RTaWmOIDm1x1JaSZTJh+WeL3cqlI3ZyNZfepkfV6yTKyGOIUCB55hPASCB4DZMkDKXpUHRs0ibzxMw0sykTfsTF6CgZs2G+MQeXVcR1Xa6kGMpL1gyugTcYD6HWBlkBjeo0VQPLPEgPtEz1NC41WnMMuhGQsO1dhesWEKSmJw+elBy9SIBCMaYTwEggeeWTqLTYMM7VdtklUF7lIXm7sFGNANOCQPKNd4V6x11mAdBLnqAZ1vHztLDaMaLsOt0pnKQaWLJzd/Ww4h70pVKRv2c2mWAADC+eqh/uEAoFglBHCSyB45FERn27AdtXZuampnkIgw6TrYe/o0a5wL0jUllkh7UVmD053oZr1PAasOLs4llznK3GR0YOYix5gS8jUFhdSWFw7sCHKKA/1F4D5S4gXuksgeOQRwksgeAxQz9RDtS0gCKSr54AM5KoybB0ipM1DAxqMcwepaoYbqZ6PLkDm0mQGXcNoNXoIGk5tOPcFLDNwqriCgB7z3AVeHOBsSbCXmTF/aGMgjjL5shULYEzviFsTCASPMkJ4CQSPCK6LtVSfrqW26786OzKgM6VCYwV2f74rydkAlHFS1mPwW3vH6RJci97uDMAfLtwurHW1WA4VUlYXnDzMVm7pTGnRZKVwZwEVFyWkixbyfrkeF3C2+COqL/cSCyVLWKssHD5QQOExf74yr4uKQ4UUHqtFjtKxZBmcrLMHDnHaXXCsAM+CTuFz6kghxq0pAxZC0TFA7MB8hva6CgAyF+twnLZQuLuQwgNl2FxjN9ZOIBAMHpFOQiB4JJAoNKVg7r55cT4176WjAhwnclj6eQZ17/nSRchuGVWM33ck21iVtJ1NlUdI7ENtOE6XcLjOiTZ6Qt/V8TiZvHA1mYt7mHHoslFi3kVBsRXS8qnZ6asfVy2YluWRf6yG9Dkq5POHSVqzB8M6M0fWGgkHR6WZD863UH/QjI1kSivf4WTKUn+7bKCyLhu1107OC8tZcryOdB2+zP1eFaqOpjh/mKQ1sm/fAd0DmbK1Sexw51L5cWaYx0ocXpnCngsG0hc7sVR1FaLplJ7ORx81oEoIBIIxzrjRroBAIBgCHNWYyeDI8U3oY6OpP/oq2fsSOe4XXQC6V/ay376K7ENazGuMnaLLbWfH6xvJLP6sT9EF4HFUU3KwOqwqadal9yy8NAYyt+zmcnEaJfZm/0YJ8y/zAHBKHkCFakEm+Yv2UKIJX/7oUrLZlgLW6Dqyi6pZnrIU47r91GQ9j8eNTwxF6dlbvpdVaTloPt9LYqwKlV/cyJctvLqmntKq/Aca9gvb4yXVU3EBwIaFbVTWZaBGovCVFMyNDiQPMNweSIFAMKII4SUQPArEJnL881R0sSrkyxay92kxV+bTXfYkvnWEeY7uawNpebO4HF0YSkO/bC81Pw1zCCyqr0gsNXMXAWesOMmE8j0UNvr+oorukC1OTp2B1bsGnjpC/0IqFFlh/ib2rvWliVB1vT5NMkeq4nF1TxcRa+To6XTUYXiZ7OUF5JXWo53SUbBE/RmAD1i3uTog3KRmJ/qf5bPtldDrkGzn6FjAqXR3RuCYfvyJAoHgIUYIL4HgUSBG4wsEl2rJej2PbUdrMPYipDQ6Tbdj1QwkP7tKNRTB9yr0f2qAM+C8WsuurXb2Hypgz5pcqq0NZD6jp3b3UmrXmcnXDLx09RwDBsD5Y33vnqsYDd2LVsXqwg/cdzdjO2PFGddZisv/v7W+PlC2q9GF+i97Fqv1p78AIPO98s4hRbeDU40AerRjdYKpQCAYNEJ4CQSPDA4KUtYT/95xMoZpZqKtOJtVu61h7WvceATzGkOvf9eqtYCFLcssxG8/TuICD9GABEjnzawvzqS8LrzYru5U/2Y9NoDiahxbkgckLMNFvyyfumX5XbZ0xnjVfJwZhoBzUfs7G5DO8j/tFG+2o4VYgeStq9GJ+C6B4JFDCC+B4JFAxrJ5KfVbSzEvHr71BXXPpJO8WI16Yj/jkvcd6GaGJ/5cZPLOKzpw2wBo+N0eXt9ZT8Hxcgbh7EI6XUBOcTrZabWYy7+gQcpFe7WCs+oXB514dVhwncXcCIasjM7VA65aWLWvGuI28O6yQWbnFwgEYxohvASCRwBr8RbyqsCwwIGMf2kgWUaO6gwcHwrUpkz2mjKHpCzZnzUr+/03fR6pGB2p82HPmWoy3ysndSD60evAvKUQFuko3FdCwed1JNoKMZebOVxUQF7xF7xbPrYW/3ac9qWRcPpD8aXLFax7PQ/iMik9mj0o0SkQCMY+Io+XQPCQ46oqIHu3b6ahrSiHpJ3VgERBUhKvHrGNdvV6RfdMPLCBN17okBjRcAE068zkLh6g7PBKfFFloXCfmez3y0mNBfWCFzEA1cUlvHHwKMnDrmQ8eNwdP/WP2uCrn+tgNtlrTaS8nkv8xr1UnshFL2YyCgSPLCKPl0DwsOOWkKPVqNwOLEV55BVbMSzLJvqYGbaWYh6rQ1ZuFy40aLqIDNdVB+o5Awhw74IsSXiIRq3uPFpucuFRaVCPkJBxVJnZdXouu7ekhn0NUpOEDKjV6iH1TgoEgrGJEF4CwSOG7LJTW2fDozHwokk/+OV1BAKBQDDkCOElEAgEAoFAMEKIGC+BQCAQCASCEUIIL4FAIBAIBIIRQggvgUAgEAgEghFCCC+BQCAQCASCEUIIL4FAIBAIBIIRQggvgUAgEAgEghFCCC+BQCAQCASCEUIIL4HgUcMtIXuDN8mSPNq1EggEAgFCeAkEjxTS+RJMr3+E0/+7fLkMk6mAL/95O9nv14529fpElqTuW5Dco12rYUaIZIHgsUMIL4HgUcFVQcoaC6WfbUDXseZfVDSG+Vr+6Of5rHfnkX3MPtq17BFbcQ5ZRdbA7/ZjOaTt/Bf+d94qCk87Rrt6w0J3key75gounBj7IlkgEAweIbwEgkcCicNv5LLp0F70XRZaVs1J58hH2WiAxM1FsHM5FWNMx7iqdrCqLJWDW5I7N8ZomR0bx394rwjeWkrZ5UfMC9STSI7RMjtWzR+vGNsiWSAQPBhirUaB4BFAvlxG0uslHKk5gsG/KnZtcR7rd1swbinFvELv2/a+iTz1EcrXGEa7yj4kK6tS9vBOlRljTG/XVkLS6zaOn97WKVIeaiQOv5IC/72S1QvUPe/itZP9wnJWH68jVTfa9RUIBEOJ8HgJBA8RUt1hTCYTprUlBEVEuSXgebSqzk2Jr2aiATKTZge2abUGXGfsjBX/kf3zPdji0onvEF1eFyVb0zCZTJRc9NVSNXcJ2ZRRMaa9XjK1h7IxrSzrt23lyxXsaTSQ+Ky612smSs/6LNj1uc13zMUSTKYd2CQEAsFDjhBeAsHDglRLzro9vp/PFJBzqDMmquFcBcTNpav/RLp4ChfpGHWq4HLqnXhG+1r8yJIHUvQEahil4bVVK4BkEud2bI1GOx9qv3MO7iTDfxVU7Exi/T49pQcyUPW3e3eR3OM1B4tk1TOZlG53siolm9qm0b5egUDwIAjhJRA8FLgwr1iPNS6T0soaSndmYt2XTeFpFwCzF6ZC4+UgL1i9xQxpS0KH52JVRI/25QAgU2+xYZipDdpaX1kMi5cExaoB1N8Ym+4e+4nt5B6DveXb0Mf0v39PIrm3a+4qkvWv7KUgzcr6lwpxjfZFCwSCQSOEl0DwECA7LtHys3zKP8tFr1ahT8ul/GA+U5p8XiBVrBY4izMwziVxrhIyTBJl5Z1B2rLkhOT4/r0yI4KK+HQDtqtdPVkuTh10kfFaYsje2vALHjlc1Sx/1xdHl6wJ75BQkdz7NXcXyam/2o8GM1tE4L1A8NAihJdA8BCg0iWyYW06mi4eEc3CdFan+YPkdUYysFH7nd+ceyUaGqFs50n0L+j9Rzj4qMjFtp/Gj/blBFDP1EO1rTMuSrpEGRoMd09hOd8hTTzIFyD5x6MRZS7juGil9nRt57+qWmwOX42ri3IAI5te1YddYohI7vGaexHJ6kTyV2iw7tyDfSyHvAkEgl4ZN9oVEAgE/SO77Jy12rBfvET0wtfIeMEnQhxVh/ngnJY3f5HOO8fySVq2i9S6fHRROrbV1PCOShUw3LZDW6jNMpM/tx9/l+yg5Def4oyewIQ+d2yhxaPltV9kouvPheZ2UF1eQUVdPRrji8xTObBKBjb9NBUaD2N3r8YQA0guXLgo+NxD5Xv+wThXNXtIptSg7vscsoOyQ5/SQBj1VieydkVin54/6byFnDV5WHv644r91G3WcvIYMD89dIhRdlH7+3qI7nIGD2iNz6PvIpINC9Q9X3OHSD4aKpJnm34CxSWctEpsMPXTJgKBYMwhhJdA8BDgtNs4V1NC4TErcJfEulx0yNQe2kPZGZi9OJnshekcf+8SS9eWUHkgE3UX0WUv38HGM2/w2XvGMM7mofqgmeqwambgxay+hZd00cLrK/Jwzc/G/A9vYv3b5eRdAMO6/ag0yRzfXsbSvArq3ksFXTo1VamoYjoKlCl5I4/sg5Xo+9OL31nYUWQOr0HjpvBGH8LLUVnA0i0lABhXbMB0rRBzlYZt7+ejQ2bKnOfhRgW1AE9MCYmZk6zFrN8cWpf8YzXoY3XBIjnkmvsWydqnfF7O6vMONpjGSFoQgUAQPopAIHhI8Cgn/zZBSUhIUD61e3yb7tUrWQkJyhfOLns5G5TmtuAjmxuc4Z9GURTF41E8Yf7rsxj7SSUhIUFJeLNYafZvu/RhlpKQkKAUf9157Je/XqlkfXg29HrzX1a2fnJpANUOt959FOI8pbyc4GvnrZ/U+xvwS2VlQoLycsGpznN9XawkJCQoWb8Nrd8XbycoW39XrzQ3exSP80vl5YQEZWNFQ9A+Df/6flC7dHDJsl15+e2TSm9V9HznP++H9QO7pwKBYEwgPF4CwUODiuRlG6C8kLLTDWTM1cP9BqxkEN8lsFul0YV4ctS6MCO/A4WohiAA38Ge1/MAA/v/e2ZgFp8ke4Bknn+28wyJbx1hniN0rp4x6yjpuvCH01SqB691tXm7b9ZgWj7vLvN7lNTzSI4Dc3Eh1reSe032GrievznOizodKmQsm9fDOjN7U4Jj1HSLN1DznAOPF+gSu6dduIHytAHeL4FA8NAghJdA8BChXpCMkUKs/2JFXqHHfmwXxq1FDGnYudtG9uJVPcc2hWDkSJXZF5/VDUflB5QAhqxNJMb6N8o2PiiywaLMkKFDTYg4VKHrN3isE/liCUkrCsLbeVEuNQcyexCXMg02F6Ahf11q59/dDdQ1AsSj9Y8rqmK1aACr1Ya8Qh9UllrnuyPWA6+S15xPzdqeh3iHRCQLBIKHCiG8BIKHiRg9GYvAesZKg2RkV1E8206HP6MuvHNoSV+WjNqtpj9fk3Rfh6rHt4iM9fMyADJ/+nxgq/3YLqoB418ahz6lhUpL8qJk1DP7rTVS7JR+9olHP7Ozhq46C1ZAk5XemRdtpp5EwGJvxgMh1+Oo2kF20U84Xpc+pNfqcfumM+rniMB6geBhRAgvgeChQkX8XxrhTBnrUspI3Hk8NOnmA6Mhc+teMh+wlOYbAMkY5/hkh3zVwvLdPj9ahikaa3kF2pdSh2z9RdWcVPYeSH3gcnwOLSfNbkAF0vky0jaXwPwNHFzXJddWlJ7UZWA5ZsHhzgzy+smXy1i62Ulp1d6h9UYCjq8qADDqx2RmM4FA0A8ij5dA8JChW+gTFy6yeSdtDK+g7AaoprLKhv30YZKW5WGcrwEM1L6fRXa5jHbMLXqtYsn2vRixsf4lE9lr00hZs4OMLfup+Tg7RCS++Fe5gJWS047OjZKVLa/vAJw4XJ3JtmT3ECTe8joo2WeFtAIy+ksLIhAIxiQRiqIoo10JgUAwAFwVmNJyyT9aQ/oYNr6u02bS3ir0/2Zkb3kRuqp1LN9pRbMsn6Nb0/sdyhw9ZKQmDzKgVqtR9SEQq3ebyClOprRmL3qVgwLTUkoCf9VQUF5O4nc7SHmrHnPVkX4D8/vCVpzNqt1grjRjHLuNJxAI+kAIL4HgIaNiq4kPDEc4suYhyOHklpBkUMf6VYIsI3lArR67gnHAeB0UvrAU8+J8at5LxdME6lgVjjoLeevysGIke4UHc7HWL84GdxrpfAkpawrYdLCS1QuF6hIIHlaE8BIIxjwyjqsSujka7MdyWF6aTOXHmWPYW/QYIjso/OulmJ/Ip+a9LsH0Xgn7eSs2hwfDCy+ijx2c6pIvl5H0+g5yi8rJNIlZjwLBw4wQXgLBGEc+f5ikNXv8v2Vy/HTukAWkC4YSGcnlQa0ZBknslpBQo36AYUqBQDA2ELMaBYIxjmqmgYxFRli8mk2rUlEL0TVGUaHWDNMQakz/qT0EAsHDgfB4CQQCgUAgEIwQIp2EQCAQCAQCwQghhJdAIBAIBALBCCGEl0AgEAgEAsEIIYSXQCAQCAQCwQghhJdAIBAIBALBCCGEl0AgEAgEAsEIIYSXQCAQCAQCwQghhJegZ7wystf3o+yWA5u7/iwQDBtyRz+Tkd10+Vn0P4FA8HAjhJcgGK+Dw5tXYXohiU/railcayJp8atUX3VQttlE0uIkSi4K4ycYJhy15L1iwpS0neo6C9mmJJIWF2J31JJjSiJp8TpsovsJBIKHGCG8BMFE6ch8KwOAgr+v5MW8/STjImdZHtq39pMOyB7PaNdS8KiiS+TN/2gELGwvh20HcwEzy9+1seHQNsADbaNdSYFAIBg8QngJQnB+Vw3Ato9yMWKnGjC+X0RyrAsLRhLnilXjBMOFjO20FYCPt6bDdx8AsP+fVqO9WgFxGejFQtECgeAhRgivhxYZ++kyVpmysbofvLSu5dZ+WI1mnZkMDVg//wAW5WN+QYXj9EmYn4lBGD6BV6L22A5MK0uQhrJc2U5JFWw4WIkGierfuDBuLSUxCmpLqzH+IpFhWoZaIBAIRgQhvB5CpPMWVpmSWP7WDmxA9JAWfpbCC/D2K0ZA4lSRi8xfJAMy1k+qMaZ7MB+wIMJsHl9sJ3ZgeiGF9TvLIGZo+59kq8ZKOukL1dBUz55GWJ+iB6+dk2fgxehazCfso90EAoFAMGiE8HoIiZ5pZPfnx8ldNPRly1dtuMgmWQdI9ZRhIH2Bb2ix2Q3WfbUsfD1deB0eY7QL3qT8+H6Mw1C246svICsDHSB9VwtsIj4W8Mo4geJqyHhFP9pNIBAIBIMmQlEUZbQrIRgMMmVrk9hxxsiRKvPIDP+5ZYgRkksAyDZyklZRvSiXmgOZIyLEZbeMSvQ/gUDwkCM8XoLwEUZPMIoI0SUQCB4FhPASCAQCgUAgGCHGjXYFBKOH1ORCcnuIjh58eLTH40Gt1aEWzgjBgJCRXE4koh8gON+DxxONVqcRMYcCgeChQQivxxYX+19Ko2QIStpwsJLshWMgt5dXRvKqgkWgW0KOUQvDPNZoqiUlLWcICjJgrjqCMZwYR1nC4ZSIjo4WHwxuCTlajSqqa/PIqB7bBhGMRSRJRt21T3plZK8K1QC6qeRyIHmiiY4eO44CMdT42KIh93gBmp7+FKdBgwZNXOi/nuj68h41JCs5L2Rh7UgqJdvJMaXxu7p/5tWVZhzeYTy3Wwqsa9mBLImEG30Sm8zx3Zk9/kkTp/H1wbD6X7geMxnLr1JYunQpaWlpLF26lFLbwDKQSd3vqVt6KNOqSOdLML3+Ec6OlrlchslUwJf/vJ3s92tHu3pjHlnq3m9kpCHNpSgAiZK1aez/KtBLsWw2saP8FNtfy6baFW45MmVvLGXp0rRBP/fDgRBejzO6VA7uzAjZnPv/fkZ5XTnlJ0L/1dXVUVNVTkHWcCQTGCwOdqRkk3z0IMldbLM2Lp64537O0RwnS385PLnHuhsx+7Ec0nZWcOGEMGL9oUvZRH5a963J7DtaTt2JPvpf+RGyFw38fM3NAEbMlXXU1dUNwEvbzQiMpKgfalwVpKyxUPrZBnQdH0xR0Rjma/mjn+ez3p1H9jGRJ603bMU5ZBVZ/b/5xUDl91TkraLwtGO0q/fIUL0zhbKfFpG7WNe5UWNAo3me/OL15KTlYA/rha5i9Qnf835kS7JvyxhwFAjh9ZijS3snxPgVrNjeZ6dWxWhIfauIgmUaxgLWA1uo32gmc24X/7FKz7bP9pKoAfULm9jmzmN75RC/GHsyYjFaZseq+eMVwoj1j4r0vyslWPpXsyqvb5Gs0hjY8E/HyWBgdHjGogf44u3JCIyEqB96JA6/kcumQ3vRd2kD1Zx0jnyUjQZI3FwEO5dTITRECK6qHawqS+Wg34ADPjEQM52M94rgraWUXX44esJYRjpnJqd6E0XLuubrU5H+qyNseEED6kRKt0os//uKAZU7lgbRhfDqhux+WB4cCZcbwErtxQdxnfqMX3rQNgvL/74/Y6Iidc3bI3e5TbVkm0yYTDnUdr1c2c6eIhtLTPGBTa66EtJMJky/LPFfg4rUzdlYdp8aQgPZsxHTp23D/FYi8IgbManZ5+U7Y6H+QYZZVHreOZofvK0qj+39ZaeP0vHmznSGmx6NwGBFvSyHDEkPS53rDmMymTCtDV7OSb5cwZ5GA4nPdnr6aovzMJlMZB/1t3eUnvVZsOtz2/BX9GFCsrJxsx3zgYwuBryLGEDN2qO57Hh91wN5QB8e+/OgSBxea8JkMnH4XFAvpWJvIZq/SiTQS1215L1iwvRCNjZ/8+hfWg/lHwR+f9gYdeElO2opfN9MSXGJ798hMyXl9u47cXitiezi4fEeyJctrDKlkWYykfR66Npz9uJsTFstQ7sm3QPge7GmUXjB9/uerBRWbS0bfP1Uet4t3ha8rTyPXeX9GJM5RjKA5n5fNDK1xWYKDx0O3OfD75up6EMwyg6f0Cq5KAMShzesx+fgr2b9Lw93XmubjAfQ6zpfhxrTa6wAkn/Wua5f9DQtNFpxDpHhCzFiXhclW9MwBepMj0ZMvliCybSDMRBmMGhqD+VgSluP76qsZC82kVdsHXR5qrnplG5NDtpmeXc5lqt9v1V1plSgAc+wXWmoERi4qJeoPXGYHVuzMSUlcfj8MN94qZacdXt8P58pIOdQl/viloDn0Xb59E98NRMNkJk0O7BNqzXgOmMfQS+eTO2hbEwry4LOKTfZKdu5iuxDoy8C7Z/vwRaXTnzHJI4exIBq7hKyKaOii9crHNshXa3l8O4dZL/Ss/0ZUnqxpSPd1tYDOew54/t5T1bwx7TkgJ8YtJ0bNIm88TMNLMpEH3ihT8GADfuNjraWKVlpIu/E6PeVcBh14aXSxrPkBR2W3QUU7C6goAriF3RpdNnOjqSl7JmZT9GK4VkqRDU3nd1FK3ABhp8Z6R75oV+xl/z7eaSsPDwmxJfatJq6urqgf0d2ZvAg8wpVz2QExsA7KNua1Y+3RkviimxenNPfmVXEv5RCvFzru8e7C6jXGojX9BwWLV+1kLR0PbqdpWQ+o6L2/dfZc0FD/tFKKovz0VzYw+v++Cn52llsGNF2ndUmnaUYWLJwdreS7TiHykp3N2JRGl5btQJIJrHLkGd3I6Z6JpPS7U5WpWRT2zREdRlhEtfsDel/+SseLOZPvyw/ZAmsvGV7+vYeqPVkL9uAfhhXbehuBAYu6tVMwUFZuU8ADW98iQvzivVY4zIprayhdGcm1n3ZFJ72RSI3nKuAuLlB7wnp4ilcpGPUdRuIqXcOo6DtikzFziTW79NTGvAmSVh2riLppeXsODY2DKkseSBF3+nt6kkMEI12PtR+5wwcF47tUKun4PiqDGsjoFMN7dq7QRfRky0d+bZ2nS4ku8hK5s5SaipLyYyzsn6FGZcXkBuoaASdNqiXUl3kInm5MWi4MBpwSB29VEXmgVJ4dxWrDo392NpRF16o1Oh/bKAjeiL/77IxBgy5jOVXyykjk+M7h3d9QE/jJQCWvNCTuFOTvvs4GV0M/qOIYUV34+cid20hvWsvFelbNmCM7b9stUZP/DN+A7Yol3dXJKOL7eGOyna2L8uDFXvJT9OD7MDOG5jLy0mfq0b9TDrl5WbeiHYhAapZz2PAirPLW811vhIXGSTO6V7+g+SMCqYnI1ZfWQyLlwQNPfr+EGzE9K/spSDNyvqXCgl7cs4jj5rMfzR3W/+xhKz3qns/RKVnw9YH++Dok56MwCBEveGVXI538+gNS3Udl2j5WT7ln+WiV6vQp+VSfjCfKU0+ITB7YSo0Xg4SAPUWM6Qt6YxR7CB2YAJAdtRSuLOQ2qsD85PZT2wn9xjsLd/WRUBHY1y1j+MHNw17m4V5ddRbbBhmdnEI9CIGAOpvdG3hMGxHrIHcj0sZ3h7Smy0d6baWuXR5AvkHy8lN06NS68n9rJz8n6lwugHVbFLjwN7QpQ2b6ikEMky6kNKCemmMnvzj+dj2rQ98bIxVRl94AbKtFgsAGRi7tK2rag95VZBbvAndIMsOswZYP7cARhINvbzGo3RsKtqA6+D6zqGkRw41mf/YbfHjRjNZu6sHW2AXZKyflQHB3oLuVO9dh4VkjuT4X0MqHZlvrcbYNY5fY2T12nSfwY1Wo4cuLmdoOPcFLDNwqrii08h47gIvohsi70ioEXNx6qCLjNcSQ3fuwYil/mo/GsxsEcH3naiN7O1mAFzFORRUjdJLtAcjMFhR7xmBV4ZKl8iGtelouogozcJ0VqcZfH+P1QJncQbqInGuEjJMEmVdwjtkyQnJ8QP70G2yYT5mxiYNwE/mqmb5uxaMW0qDZiODCt1cDdoxs0SUivh0A7arnZ6svsSAtvuGcGyHPLwdpHdbOtJtrSJxRTbpC7vc8CgN6WtX02F6dT+GU5c721q6eg7IQK4q6wzRaPPQgAbj3G711qWzf50B81vbsY3hFB9jQnjV1/hkl2ZZapcvLwcfbC6B+ZtY8kxn47ou26ittFD4flnwMIRkxVLV6Zuxniik4FBFeEODbjtlVUBaJjqPA8uBQgrfL6Ss0hYUc6A2ZZANFPzXkw/JLKZBoE5kb9GGoE2u4pwH/4Jw2zlZ5fsx1FvQcaIKcopdGDa+iaHjlrtdWE9XYCk2U1hcG2h3+Wo1BTvN1DZpWbIMTtZ1Gg6n3QXHCvAs6IzNOXWkEOPWlLC9I7LLTm2lhZIDhZR1mSbuqDrMjvctOCd1M2LSJcrQYLh7CkuXOJ5ejZg6kfwVGqw794Q5LfrxQL1wNfvXGYK2lWzeOEzDsjL2c7VYTpQEv09kB4d3F2Cpc4QYgSEV9W4HZbuzfYHwJhMmUxo7DlWHvLNsJwo793klm8JiC7V1tdSe6xan00+fdWiNZGCj9jv/GbwSDY1QtvMk+oCn38FHRS62/TSeARE9cMNdXZQDGNn06vCEkAwE10UrFeUWzO+bqXV0xg1VHyqg8JiV6Jl6qO60Bz2KATzIFyD5x6FibLC2w1FX5p9U5PuXtnYHFZe79ZAmK4VrO/fJ3lqI5XQttadrsTdBb7Z0dOjvmXPyfFoGLsvZwHMgORuAMk7K+oA4c5wuwbXo7c6Yuy4kvr4BDdUUlo/dj9oxkLleou4TX/zDT/6882GXr1opAYzpXWY3IOO0n2X/lgKswLylS9DNUQEylv+WTd79fNIX6wAZ66/NlDQaSH89td+s1tLlWqyA4f4pXk/J6zL8Y6Z2+3HyX+l4kDQ8v0IDxSWcbcogMbaHwmQHJb/5FGf0BCb0edYWWjxaXvtFJrrRfha6oTZlsz/rC9Yf7BzzN7+1kcTPj/R8zWEgXa7F5zfLxDin5wt2nPZND15i6nwRy02XsJ6rZk+Rz1sW/1IdqRpoqPuAkmNWzs40ceRXpZS8sBzLS3Wk6yB1ew01f9eZ3Vg+f5jc8g1U7gz/Be+02zhXU0LhMStwl8S6XHTI1B7aQ9kZmL3444ARMyxQg+TChYuCzz1UvtfRY/1G7GjPRmy26SdQXMJJq8QGUw+S8BHoS4Mhce0+sj9Jw9zYscXG+g1mKj/OHtphRdmJ7atzlOwrxAq4FiaybbEO2VHLnuISqNTxv36VgavoLNIaA2r8or68AM/Bym6ivnRgdZPt5C1ejoUMzJ8XYVRLWP7bSvL25VBmy6fGPxxkP5HHqnctbCoqZ7WxmYLXlmPebcUMaNLy+WxhZ9xR/322kneO5ZO0bBepdfnoonRsq6nhHZUqUIbt0BZqs8zkzx3mjuS1c/IYMD99WGP0wrwZXLLVU/1JAWVngCYDdVuTQW7gg30lWDnLi5++CY2HsbtXY4jpKgYy2BuYeVHNHpIp7XHUJAzb0Q37iTyWv2shY+sRipYZkM5bWLkmj9zXy8gvriH9GZWvH72UjWVRLuWnM/FUFbB0ixlruRnQsK34M2ZLPdnS0Wrq/p+5yhPvkH8oibzyF9mbpkOXso2aqndQdXjlZBtbttjZX5nfs1c2Vs9PgJLfVCIt04/+NfeEMtrcPqW8nJCgJCSsVL683bm5oWK7kpCQoGz93aWQQ5z/uktJSEhQPvraoyiKojRbP1ASEhKUhJ8XK83+fTz2T5WEhI3KJU//VTj74Ubf8QkJyqdf+0po/v37SkJCgpL1YX3Qvpc+2eg/d3PPhXkuKRv9ZfX/b6Vy9t5o34DeaFDeX9Ktvj//SGkeZGlnf7NSSUhIUF4uONXLHh7li/yXlYSEBOWkPfSmXfrdViUhIUHZ+ElHf2hWPn0zQdle0eD71XlKWZmwMagPKYqieOwnlZcTtiqXBtXOHuXk3/r7RUed7tUrWQkJyhdORfFcOakkJGxVGjr2vhdc7/oPVyov//ps76V/96mSkJCgrOzWxzp3eFT60iBwdrwXOv+t/M2XD1CgR/n0zQQlISFLqe/eTvaTvnO8fVLpuIP1v81SEv72lKIoHuXkmwnKRov/Lrd5FE+X2+z5+iMlIeGDfp+LS78Nfm+c/TBLSUhIUD6wdu0zDcp2/7X63m3NSvHPE4LeY77zJShZv73Uy5n67rOKoigN//q+kvBmcUidL1m2Ky93aYMBYff15eLvwjy64aTv/vZxPs93xT2+g4eNtkvK1oTg9m7+ulhJSNiuNCiK0vC7jUrC21901i/oefcoxUsSlPetvfeEPm2Hp973rL9Z7GuP5i+VLP87N6iHWLYHbJ1H6ak/NCsf/dzfz/0H9mVLR62t+3zmFEVp89mfj7q3571LyvYlLyuf9tnPOp/1ru/E7s/gaDLqHi+XtdrnYZq/hPjYzu1So89NaDSEDkupZ88FoP6yExbAnqxC3x9iOuMsnLZaSOs646Q3JOos/himjaVkLPDr495mH/XnJ1bp2VtTE7Y7eSBrTo0sOjZ8tJfqtBwCfq8LdpxeUA94ZpaLU5/4SslY3PsQhtPuApKDUkN0oP/zDAxYqP6dFXmZHhUy1jOQuN3vjdQkc6Qq3jczpiuxRo6eTh9EnQFUJC/bAOWFlJ1uIGOuHu43YCWDeA2oNOkcf+8SS9eWUHkgE3WXOAl7+Q42nnmDz97ra7afLx6m19igIetLEpbdeZTYQD2l9+OlZif6n+Wz7ZXRH/pBk8zH72WTttkc2GSzu5AZhkSIc5PZMB8Kq05id6djiAHnd1bSF+sBFen/dJxLry7lsK6S1Qs71zeUL1t4dU09pVX5A/yqlqi3WAEDC4O8vzoy1hkpK7JScc7O6gV6iPHtL7X5Llw1x4AB8PQaNNZ3nwXQLd5AzXMOPF6C3nPahRsoTwsjKbJsp2BDHvVTtIHrlq7XA/DBtnVUPxHYirM5nvx9uSFeLbmpGRdgfMEwJPfTfqKAvE/q0U7p4040S0j6TIq29jJRK0pP5joDlqJqaq/K6J9RITttsDjZFxf1yl7221eRfUiLeY2x0wODjGXnq1h/UUp+X6shDGCMUb5q843CJAe3j+7PMzBQhu1CNXZ3Jnr/EK8nsIyRGmOqAS6Ax99n+rKlo9bWfT5zQJSODSdqcLi6xwxqebO4HF2YD9ywzRB9QEZdeJ0t/wIYmBtUpdFjACSPhPXQduo37mevtIecgxU0eDPQS9Us3Wpnf2UY80Sa6im+AMRlU76m0+A4LvqEgr7fVAk9VVA1ZMbBZDI90PF1dXWDP1hjZMl8sPnzheUfeyd0xl44uOopawQwkLhgkNnu1fFkLIKCM757PNtWRtmifN7pGk4RowlZe1IVq3uge6FekIyRQqz/YkVeocd+bBfGrUWBANUHNmL9MRR9ye3gZHE11jgNmj7Sg7gaXah/GrztQfrfA/U9QPPjRIyY/fnbMij9u+Ga2awmOcNI4e5qzl4Dwxwr248lU/Qr/13uzQgMWtRHE+0XIt0TqmrnxwNW1H4VPWUKgJUPPrOyd4URudGBDUif2ft7qb8+C6DShD4Xal24/VWm+YwNa5yz83lr9AVouC5YqW/q2OrC1agegXhYGesnJdgcGpx9pb9pdOGa0vd6B/FJGVBUgKWugcxnZlO2uYxtR98J/D3xrSPMc4TGuxqzjpIerhoIB7+gcnbfHqPlecCGGtU4QOX7krIVlWBdYcSolrGftwEZaIdFdQxVW/fzzAGgQqfp1ktj1MM80W5kGF3h5bVTUe7rxOnJwV/Z6jjfC8hqayDzmW5f4DFT0ALVO1dRTTrH6xLhmH8uiUfi8Es5ZL5fTmIYz4HLWokLSP5PSzpfIm4ru3ZXA+msTh7gbXbbyF68ivDSSRo5UmXG0EeMw4Marweh9kAOe/yiK2PncdLnDM7sOawVnV7NHu6Jo3IHeY2redOggTPV2B0yhpAYEzWJf5kMZ6qx3XBhyy9k2z/UDP8yEDF6MhaB9YyVBsnIrqJ4tp0O7o8PZsT6YKj6UoyBvYPsR6PX/yQO/3J94Nrzj70Thvd68OhN6YAV6xUHidY9zN66rdtHRqgReBBR75NwNmx2iWRN50MRHT0ZAKckASrS3zvOKdNSLLuzWXU6GVtVNcasfN5J6+O9FEaffSBUBvLr6ui63oB8sYSkFQVsOlTD6gX9t4oqVosGsFptyCv0D/gcq8g4UDfgJaR6LOnZ50kGqq12pBesFM7fRk23d5Em5NlWoRum4ErX784hvdXVKRHN5Dig0YHU5lvu6fjuUyzdYiE7xU7yfBvVF4zkF28KTFTr05aOYlv3/8w9OCOTi27gjLjwsp/YwfJfe9j/cT6JkjWQRuLFbp1bqzcCJTicfX8vZezegA6wSRLQwJ6NKVQvK6Duhc6HQ3bZOWuXmLfAiKabYar/ty8AA6uTOjqkjOUft2AFst9/O+RlL3kkQIMutpfPiRgt6cuSUbvV/XrwpPs631fLGESqM7PevxisZlkB29IG/51R/7k/jcTy5NA2aaply5Yy3jj2Djp1ImDB6fbQ04DS7IXJQDV5a9Pgx/nUPWgAsNuF9fwl1Prn0Wt6K0tF/F8a4UwZ61LKSNx5fMheDh7/8iC9elUfkb40GGoPrOsU/bsHL/rDRTXreYyAZfdSLI0ZHB9KoQLdurOK5xcb4IyNwmO1ZJtSA3+5VOcLe+iYYCJd9L0jC45VkqqLRvaqwghPGL4+2z89P7shzNSTCFjszeEeMTKo9CQvguryXFLKNeQfKx+yuvVtO7qJ+lnxGABbYyHVjmzSO16/0iXf6MGijkkJki8V0rICan6Visorh8QchGtLR5rhe+Y8SG6A+GHy+j04I/yqlqg+XAaNYG96hylVfoO8dXVIAj+VfzmasqqzyGu6xQG04ZtqGreBDSm+Hqn7cSqwh+ozmZQfSO2ys0zJG8vZ0+jbv/JEl5lRXjsnj/ndxv5OXPH+OvKOucjcWepfgyu4/r7YjGT02t4eRw2ZW/eSObINO7Q01bJunT9ujkwO/ip18GVJtRwu7/jZgaNJ6xuakZxYz1Sy/d1CXGSTOEeFJupFwELFOQerFxhCilLNjccIWBs17N/y4Ov02Y5uJHufDTCwv48Zm7qFqYAVF9l9exoGiOMr3yxOo17byx6PQF8aBD7R7xvq16zYy7aUERhciNGRvgisZyC7aFNoQtEHQsZ23hcD1TG0aFj2Dsn7sqkuz8WcdpzsxTrkyxbfTOLF28hcoAZkTm7LA6Dstx9RHwMToqegUqvQLXiR1IW9t8tw9dkhI0pP6jKwHLPgcGf26KmVnHcBsFo6Z5UOPyqeX2yEM1bIyid9zlCV27ftkG/YqAdw+5ZAU8UYeXdLMqt2V5P3N2aM/zMbnUrGss83637bf3kNNSCfLyOvHIgr40BRPURPYIpKhUqr48U/T0Wn7seWjmZbD9cz57ZTcQFYbEQ7Yh8cA2OEhZea1P+Yzp6tFgpeTwFAk7aN/GU9KN0oHau3JFO2u4AKR2an4geI0WECTLuWB4YHo6NkwMD+ytyQOB9tPNAINH6B3Z3dmV4iSsuLywxYjtlY/9fZaC5Ycc3PYO/RIpLn9jQmVk3BBcjYObzDHqOLC/OGjnX4NOwtzx38A+GoIG1pbiA9R/W+HJbuC93NuPFF3z3TpbJtEezYXYLj9W2h543RogfU24sGndaiKyqtHrABNs5dlUiM7fl1o57p63z5R9cO3QvJ66BknxXSCsgY7qn7DxNNtWx46598P8dlc3BL73Ga7e3tREYOVSpCNbP1wJR81pqG0Ox4HZjfWBpYV7UwK4WG7aVse8XI3vL9FLyznsLNS+n4zEnOKiD/rVS/cVTxfDJwAaqLzQSnMS4gfedx8nsRVcPSZ/tiEBliX/yrXDhWQMlpRzdx7aJk88rOxLkXCkgxlZF7sIjMhYO/mnD7i3auHtBRui6x/0LDpQ/b4TptJu0tfw+4sIcUk53Smm0YVuzFrCoge2chS5MCPYT84nxfKglA9ax/8k5jNeaD3XtIBqWnt6Hvy5YOU1uHx/A8c45/K8MK5L+dOnY8qd0ZjamUHmeDcum7S8qlK/1N62xQdiUkKAlvfhoy5djZ4AzecM+pNNzufYqps/aD3qd932tWmp1Opbm5rymqzUrxm8EpKx5Fvvz1ysD0/fd/7wz7uO6pFAZNwxe+ac2fhE59bqjYqiQsGeL2v/2lsjLhZeXLPgr94m/7SPkwSOp/m+Wb7vwod6YB41Q+WJKg/Of/vF75sz9bo5zqp/t9//1Fpa2tTVGUcPpfH+kkFEVRGk6GTD8fKTz3mpXm281KyOunuV7Z6k8D4LnXrDgbGpSGhgal4col5eSvX+4zVclw9Nm+L6JB+eDtrT2mgumLUwXB6RuGk679pVfafCk9hjblwAPajjaPr3/cDj360idblYSErcqlZo/SfNvp7x8NyiXrSeXlhJe79OfebemoMRzPnD91ysoPQ5+NsZROYvTzePVHgy+fz8Y+cpD0R7M/X9IHteELie48DobS6c9dlpCQoKz89QByJt37UklISFC+HKIHqOFf3w/kIWpuaFCa2zrysvViNAeL37CF5ofyKA1XfH3l0icbh1xs+3IDBeeo8XrblB9+uDyEZ3n4+PLXK5WUlL9QfvGL7H5Ff0vLfaWh4YqiKIrSbH1f+bM/MyvX7tzu44hg4dXW1qo47F8r3166GsiXNhZeyF259GGWkpDwco8C9NKHK5WXg4TVwPrsvXt3lZaW+6N9ib58Tf3k8xoKuvaXEDzNSkNDs9KRs+2h+cjy+ETGywVf9vi3lQkrA7m8FEUZElv6wFW+3aA4m5Vheub6FrhjSXiN/XBcXTKfHSsga9lycjjO3lcGHq8g346m4FAlqQsG5860n8hh1W41+8v3YhyTaXCHAFc1G9/y50yav4Git8J3s7vqLEAms4cokFG3eAPH34OlrydxALgGgIaC45/1OQN0oMhSM4m7S8lPCR7qls+XsHTNHv9vmRw/nTlkwzXy5TJS1hSQW1QecOW73fe4fv0HWlvHVvDrSOI6Xcj6gzb+7M8Sufvjv+4hvjKYxsZrzJr1JABn/uUzUn75Nncbr8GU2H7Pdf/WdVwqmf/zv37LP3/roK7uDJm7j7N6kO+HYSMawEVOWhrpK1aQ+JwOj8NKWVEJNjIpremMgwy3z3q9Xm7evMaNGw70+vlER0/ovx7DSZSODTXH4a+XkrQZat4bnpQhXftLd2y/XceqfZ0xheVrDAMpOoRLl2x4PC3Exxu5fHLTA9uOruVFRESE/N1VvJ40Wzor/jIRncqD9d/KKKmykbGztHPpNQjblt6+7eT+fTcQQWurh8jIKHS6OURFBcsFSbrD7dsuxo3zbW9ra2PGjJlMnDgpaD+3+x5NTS4iItr5cu8/UKNM5uzZP5Da7Zlrb2/H4bhCa6tMREQE48er0OnmEBEROjx8585t7t6ViIyM4P79+8TEqDiT9zoF5FL+ka/vd543Eq+3jVsT9Wi13/TazrLs4cYNB3Pm9B/k7/V6sdms6PUGJk4chFEabeUXNp7mfoYCh/HUt52Kp+3Byxm7dM1Sn9XnsFsoTt+xw+DC9ty+rPz27zcqG/M/Uupvj2BzOL9Utr+ZpWz/8Auleajv+71mpbkHr92dO7eV+nrrgIqSpDtDut+o4c9S/xd/8e+VX/7db/v1Lra03O/0Dt7+Unk5IUH5+zKr8u235/s4KnSo8ffHipQd/6NI+WIMfAH3TLNy6sNdysqEl5WsN/0hAD/fqBT/a33oszbAPvvNN2eVO316CEcaj9LsDO8+DLQ/B/WXHnDWfqpsfHOj8lHF0Hi6rly5pHz33Xmlvb19SGxH1/K602w/pex6e6WSsCRLWekfrdhYUKzUO/t4G/dhS12um8p3330TtO3qVbty4cIfgrbdvu1Svv32a8Xr9Qa2tbbKytdff6W43XcD2+7ebVa+/fbrQN3P/naX8va7e5R/+bcaxeNpCSrz4sV65cqVTm+cw3FFsdu/DaljY+M15fr1hsDvd+7cVr766vfKTceNXs+rKIpSX/q3yv/4H/+gHDkX7EK+dcupNDR8r5w9W6tcvBheH7h69ZLy1Ve/V9zuwQ3BjH2PVwcqNepRipRTxQ5BTqYxTPXuLYF18XIPFYWV/wxAdtk4vHMV5kYw/lX8kH+pqmKfZsW7e1kx0g2iSWTbgSEMrO1KTP+pIcKhvb0dl6uRyZOnDMl+o4eDwjdycAEvLPgZ/2nzsn7b5+bN68ycqcN1sYLtK3yTN+LnTAXuDejMTyYuISsRZs8eY56uAGqS1+SSvCa3/10H2Gd78pyMLirUmv7fIIPpzx39pTc0pgz2HhiKzFQ+unpMhsJ29OWBUc9NJve9ZMLoIZ30YUtbWu7j8bQEt48mDpvtHB5PS8BD6nTeQKudGTRZYdy48cTGanC5GnnySV+db968wYQJMYH+ZlyRy3vAN9+c5fZtV+C+NDc3IUl3eO65fxcoLy5Oxx/+8H+4e1di8mTfM+rxtOB03uCP/uj5wH5TpsSyYMEiVKrOIZfu5wVQee/idkeh8jRBlyl406ZpmDZNQ3t7O7Lcf+avO3duEx098YHu6cMjvB5zJIcdh8PB2TNnmf3KBpJnOqg4aqHOOZnlWZnoY8BeVcbJ0zZmp6wmwxTekKyjqoCc4s7FsMuObKf6vtTvcVJzPdYznRmcTYYxOGX9EaW1tRWH43va29uHZL9+8co4rjbQcMXGuXPNpK/LRNdkpeSYBac6mTdWJKNGovZYGZXfyaSsWk1imAklq3dnYW6ESZNiUJQ/cGTXFqT7ve+vUo3niZkz+PpfSrEGFtE2MEcbA57whNdYTaoo6JvB9OcOQzp+/Mh8tXcY7/b2dmJiJo258vpj1qzZaLVxQdu83jYiIiIYN258YJuiKLS0tIQcHxUVhdfb1qX+Xm7dauLJJ+d2229c0H63bt1k8mR1YNiyo6yYmEncvn0zILyczkYmTQr9UOoquvo6ryzLRCheeqKnIc3utLW10dzchE73FA0N3w+6nYXwehiQHZS+u5zCMwBG9iZVk7M0JzC9fG76T3BWrmR9kV8IVepIPbG6f8+Ko4KszSVBm2zlls61GQfAPN0YzVT3kNHScp/r1xtQlHZkWUarnYlGMyPw96YmF7Is096u4PG00Njoi4AbN24806drB7SfJN2hoeF7IiIiePJJPU7ndSIiImhtbWX6dC3Tpvn2kx2n2PV6LmcnT2L+SxtY+Pvf8v98+L/R659mkvsPNNyeS82qDAr9QuiLaSZ+99d/hM32B2JiJqPX/6jHa3VUFpBT7Ouzzz0Xz9df27h3r2/x9Cd/8u84/v/VErxbPFMnAp7OL2Kv14vH08L06Vo0mmBDEh3mW68j5mP8eBUREeDxeJg5UxcSF9XRvtHRE2hra6W93cu4ceNxOhuZO/fZAXsb29u9XL/eQGRkJJGRkbS03EejiQsxOLLsweVqDBix27edyLKM0ZgY5IlobZVpaLhCRARERkaF1P/+fTc//HCZ9nYvc+f+iBs3HIHyNZo4pk0bmNdmoOW1t3u5du0H2tvbiYiIoK2tjSeemM2ECZ1ehXD7fXdu3rxOXNysoG0OxxUaG68RFzeLadO0XL78LRERETzzTDx37zbzww/fM2PGTJ544smw6wc+UeD1tuFyNTJ16vQehZLTeYMbNxy0t3v9MXYTuXHjB5qb7zBz5uygZ72/8ob6voFPfHQVMa2tMteuXeXJJ+cSFdWZ32f6dC0//HCZ6OjowHtCURTu3LkV8HaBz1vX3YPW3t5OS8v9oPty755EbA/ewejoidy7dzfw+927d5g6dTrNzU00Nl7D6/XS3t7OzJm6oH7Q03kV7jF16hO0jRt8oPD16z/0Gis4EITwehhQ6cg+UMop03KsWMlZk8OGouNsurqL5TurKViRBotyOV6uJy9tPVaduv/FQb0OCrrk2HowjH1kfheES2urTGPjNebM0RMVFUVrq8w335xl6tTYwNdmx8vJ6bwBKCFGpYNw9lOrp6LTPcWVKxdpbm7i6aefBXyBoxcu/IGIiAhiYzWo5qSy9+g2tn1Qwxcn9rD+0z9if/luHP/1p3zlSWXDf1jN3F+aKV9gJS1rD/FqNe3t7bS1tfU+YcBRQdYWn+iPiZlIZGRkv6Kr1/0WGdBGwa22VpzOG+h0T/kNZCv19edQqaJRqyciOQCsrEryrT/5X4o+5k9n9Rxc3tbWysWL9cybZwgIFVn2cPFiPc8+u4Dx4333o729nUuXbDz77B8FvCoOxxUUReHpp58NMc79oSgK331Xz4wZMwMG7f79e3z77Tc899yPg4zf999/x7x58YFtM2fq+Pbbb2htlQN19nq9fPvteebM0aNWT/WX5+b69YZAORMnxjBzpo4rVy5y8+Y1nnxyLpGRkbS1tfLNN2eZMmVqSGB1XwykPEVRuHjRhlY7MyAU7t9389135zEYFgbaNNx+35XWVpn29vYQb4hO9xS3b7uIjZ3OxIkxxMXN4u7dZsaPVzFtmpZ79+4GRFe49QMCnqK2ttZe66TV+oLPL178hujoCYwfP55x48Yzd+6PQoRVf+UN9X0LbrtWGhq+R5LuoNPNCfl40WjiaGtr5cqVS7hcN9Fq42huvsMTTzwZdB3jx6tCvI0uVyPR0ROYOnVaoI1bW1t79EqOGzc+6B3S0nKf6Gg3ra0y8+YZiIyM5O7dZi5erCc6OjrwcdJ5XpmyzUnsqIIFC+Yzb55Eq2pwYRe3bztRq6cybtx4vF7voMoIXNcDHS0YMeTLtsC6dem7j5Nt0mE5Vu/fkkH5gUzU53wLCif/1NhvvJXtyBZKGCLiUtEN4WzDx5XIyCjmzNEH4hI6Xh4tLS1Mnjz+AUvv+7xdv+KioqLQamdy48a1gMGzn7cybVosXm87BcfNJGoc7Djfxmnl/8CS7ZhXGLEe2AVA6guzUalU/PEf/7teElZKlPxNp+hfsMDA11/X0x+97Wf403jG4xNGTzzxZKD9fDEn03G5GlGrf8SSfyolueN96YXWyN4zA1+79gNTpsQGeYdUqmimTJnGtWtXeeqpeYDvSz0qKirIaKjVU2lo+J5Zswae9tzlakRR2gOiC3xeiPHjx9Pe7g2ILEVRuH/fHRTDEhkZFTK04nTeYNy48QHRBT6DHR0d+mkWGRnF7Nlzg9rP1//u9zi80x/hlNfU5KK9vT3IOzNxYgwxMZNxOm8Mqg076PBq9YRaPRW3201MzGS8Xi9ut0/My7InxNM28Pr1HT83adJkYmM1NDRcYeZMHYpCP8OIfZc31PcNYPz48cyd+yzt7e388MNl3O67zJkzL2ifiRMnMW2aFq+3jcuXv2Py5Cn9zvBrabnPrVs3mTfPEKivoviGjnuKO4yIiAgMLbe3t/v7/X3i4xcG9p88eQqxsdNpbLyGXt/9elUs2XqcBb+8y23pJtOnzmHmvIELr9ZWGbf7HrNnPz2o9uyOEF4PCc7ztf6fNvBuig68dmq/8pmuTce2oQFqT38BwJIfz+63PMOaI9T81VClL1CN3QzBDxFRUVEhL5/IyEja2x/s6yqc83ZnwoSJ3L/f4VmSsVWf4sq9H/HTv9mNUXWD+99bOdUILlwc3/LngERdhQ1Ix+hfV7Gncn2oyfyohte8vq/5Gzcc/b7Q+twvSsW9ZicxMZNDzjlhwkRu3rwDgGaOPmhVi4aGK72er6nJxVNPPROyffLkyVy5cgnoNELt7UrIfpGRg1vu4c6d20yePDVo24QJE4OCicFnkKZOnc6FC38IeGOiosYxadLkoP0k6U4gPqY/eup/ERERg/66D6e85uamHkVHdPSEoCGmgdIx5NtbuozJk9XcvdsMxNHe7qWtrRWvt41796Sg9hqu+ul0c/jmm7N4vW3o9fMHXU647TxYIiMjefLJuXz99VdMmXI74KW6c+c2TucNnnkmHvB5Ab///ju+/fY88+c/FxQP1oEse2ho+J558+IDHmNfXX0fZ4oS+hwpSuhqAxrNjJDrjYmZHBh+DkGtIbKpmR8//ydB5x0I1683oNM99cDt2YEQXg8FMrX+habzj61GBchXbZQ1+vLOrJ4DIHHuExuQgTHMJWhU/a+2K3hM6RiiaG9vJ7KtgZPlLqp5kd+++8c03brBtxev88f/Ppknl/4KHUBTPV9cANJeDG9B5igVqihwOq8za9aT/fbF/vbrbZAyMjJqwJML2tvb8Xq9PQ59jB+vCsSVREZGolZPZfz48UjSnYBX6datm2ENhfVEW1tr2Mbh6aef4c6dW9y8eYOGhu+ZOnUas2bNCRIbXm9bj0ZwrOAbRmvj5s3rQdsVRRnwMG1XGhuvMWNG7/dg8uQpgZioiIhIYmImc+/eXTyeliBv43DVb9y48UydOg232z0GZ5gGExkZSUzMJJqaXAHh9cMPl4ME48SJMfzoR89x4cIfevxA6hBdTz/9bFAAPRAI3G9tDR1SbWtrCwwVR0ZGEhEREfCQBbdncLB+OOcNF19Os45hbh8ddbh162bAsz6QnHhCeD0MSPWUVAHzc0n2exMaTp8E4I2f+tfqaqqnuBE0Welw3oIlOjWwntdwIUsyqtHK8SEYVnwzmXzB3ZKtmmrAuDGTZ6dPh+mTOb9nPffa/oI/meqgpSWWVlstNiB7qRFbeRmqP89A38/wc1tbG21tbf0asN72k90yqpi++5/X29aH561nfEHtUT16Gtva2gJB7+AzwNHRE3C77waGq6ZPnxE0tDcQus/26o+pU6czdep0/6SCRi5c+Jof/WhBoK2ioqKG3WP6IERFjWP8eBUzZjwxZGWG069Uqmja29u5e7eZiRNjUBQFtzvUgzUc9QOfMY+KGkdUVCQuVyNa7cwhLX+wOJ03uH/f3WMKiw4PWltbK62tcognsCNE4fZtZ9B23+SO73nqqXlBMWetrXLg42bSpMm0tLhDztnScp+YmE4v7oQJMbS0hE579nq9IR8Y4Zw3HCZOjAlpD6/XNwFm+vQZg0qgOlQrzAqGEdf5U9iA9P/4on+mokR1WTWQTrLBt0V22HABs5ssLF3zEZqwg91lLJtN7Kh0hF0f6Wot5q1pJK0oof/EE4LhICoqKsQ17xs6Gfh+PXj4cbvvBoZc6i0nefJJHSuS/J4AqZ5Pf3+Xupg/ZU7cDO7cuY3DVgdocHyex6qtLqbE+F5OPQ0fdHDz5jVmxLaSY0qjwiH3vV8Xw+eos5D3iolXPw6O9+rJszXYOJepU2MDQqp7u3R89Xf8fvduM1OmxDJjxhPMnKnrUXQpisK9e1Kf7QEdQ2A9P1Vdv7g78h51EB09gdmzn0Kj8d2PDmJi1D0aqra28MXdYK4jXCZPntJlSLt7W4duD6c/d+8vvZ9bzY0bDiZNUjNpkpo7d5pCjPdA6xcuN244mDVrDk8+qef69R8GfT+Gmlu3biJJTSHbPZ6WwDB2ZGRUYPJKdxRFYdy4TtvTESPmmzA0Lmi/xsZOL+L06TO4e1cKGh5ta2vD7b4XNFsxNnYaknQnxOvl8bQEzR4O97yjhRBeDwHOy3WAgYwX/LmyZAe1F4BlqZ3DOtG+zm49Zqfg2EESYwdwAo0BTUz46SCkJpgcA8Sq+p89KRgWOr78OoxQU9OtHmcwhbNfS4sbSeo0Xh3eE9/MLgm7zUbrlFeZpdxAURTkxnqsQMarRiLb24mJmYzKv7yNxW6itCYbtezhD3/4v3z77fke6+/1epFlmYkTVGjj4lFHq/rer+Or0ivh9KqIbgRtt9lq9+/fC5pC3toqI0lNgxr2mzVrDrduOYNmVLW1tdHUdCsooDomZjIq1QQuXDiP1VrL119/xcWL3/hTBnQaB4fjCt9+e55r1672eV6t9glkuSVIPCmKwrVrV4MMS2RkpD/tSLAIafffjw5mzJjJ3bvNQd4Ep/MGbW2tgxJP4V5HuEyfPoO2trYQL0lj47UeJ2b0159D+ksfTJ48hba2VqKiopg0aTL377tD4uEGWr9waGy8hkYzg8jISCZOjGHq1Olcu3ZlUGUNNdOmaRk/Pvi5un3bSUREBFqtT8xGRkbyxBNP4nBcDepDXq+XW7ecPPFEZ07HH364zPTpMwLpXTyeFlpa7tPQ8D1RUZ3tN3XqNNTqKUH96saNBqZNmx704TRjxhNERkZx7VrnrNy2tlZ/Oo6Bn7criuINOyyhQ/gN9gMkQhmqTxfBqCNLEqjVAw909wIDjAW2F2ez/MN0ak5kisD6B6Sl5T4OxxUk6Q6zZs0JCIXGxmtcu/YDkyermT376ZChk+vXffl/oqOjUaunMn36jB7L72u/5uYmrl27ikYThyx7/IbLl6+qq6FvablPfb0VlSqaqVOnMX68ivb2dsaPV/lyD3llJDeo/UPPra2tXLhwzp/Ha36PdZoyZZpvuKKP/he0XwCZspVJlKQf4Yh/XT2fYYxAllvwettpb2+jrc3LzJmzgtaOUxTFH6+j4HLdBPDnTopgxownguJtPJ6WQB6vjmn6Wu3MoFiO27eduN330Omeor3dS0tLS2Dm1oQJEwIzwZzOGzgcV5g9++mQqfndkWWP36i1+z0wCtOnxwUFzvd7P7pw//49HI6rREVFBQLwXa6bqFQq4uJmERERGXb/C+c6Btqf29rauHbtCq2trYwbN56IiAimT9f26qnsqz/33F96piPnW0c80qVLNubNC12nMdz6OZ2NtLe3BfWryMhxgbQQsuyhsfEaTucN5szRB565hoYr3Llzi+nTZzB79tOBofH+yhvse6M/bt1y4nbfJTIyKuDV0unmhHgDm5ubuHPndkD4er1t/nQZnaL3zJkvgZ4lxtNPPxMUTxfuWo2trTIOx1VA8ad2aCMubhYTJgzuvE1Nt3C77+J03qC9vR2NJo4JEyb26DVtb2/H6bzBvXsSd+7cZtIkNVOnTmPGjJlhJWDtQAivxxlXLXlvrMfSaORIjRm1rZaG3tJ6e6cQbzKg9htIe/Eqln+YIYTXQ06H8DIYFo7YOdvbvVy9amfyzf/LynUFuBblUvNPS6ivq8fTRYApioLsuUf0hMkQPZvEhR1ftDIlK5Mo6yK8RotvvjkblOurA0lq5vr1q/zoR8+Nav0eJzr6VUc+OoFgrCKC6x9nNIm88TMNlt9noldBbdV+cn7nwRDbfUcntqZEjhzNxyDydT1yjPS3l6LAE088SfTTT7GCAs7+LBEVEqf25lGGlo5v0XHjxqFSjcftuIozfgOfvZcx5kT+xImTaG5uCvk6vn//HlOnTh/t6j1WdPQrgWCsI4TXY41EdZGL5J2+hKvJb5mpe2u06yQYKdzuezidN2hpuc+1az8QFzdrwDMAB4NvyCsKpFqKgbcXzoYoFRs+LmfDaDfKAHnqqXk0Nl7D4bjSJQWHl6iocYNOKSEYHIF+JQii1/xWXYiMjBwzMysfB4TwepxpqqcQKDDpABnLziz+8WvoaeUzZ1M8+45uEx6vR4iYmEk9xrSMFK7zlbjIIHGOCmQ7O15bR31sj70Pp/5tPtuZPuY8Xr5A4/4TFgsEo4X4ABh7COH1GCNdPQdkIFeVYXspg+RV76C+4enFuEUj1sEWDCUN576AZW9yqriC1BUvsnp7Ps7eUk7FaEP7ZfRYk2ECgUDQP0J4PcZIzgagjJNyBnvVgNpI8tz+j7OeKGTXbhtQxp7i2axfkczgVgQTPM447S4oL8BzsBI1KtSmRPT9HeR1YSnaTsEFoGwPJbM2kbm436MEAoFgzCBmNT7mhJP9WyAYFrwysleFWLlKIBA8TgjhJRAIBAKBQDBCiMz1AoFAIBAIBCOEEF4CgUAgEAgEI4QQXgKBQCAQCAQjhBBeAoFAIBAIBCOEEF4CgUAgEAgEI4QQXgKBQCAQCAQjxP8Pgk7tuZqKPZ8AAAAldEVYdGRhdGU6Y3JlYXRlADIwMTktMDYtMDRUMjM6NDU6MzIrMDg6MDBLD3QbAAAAJXRFWHRkYXRlOm1vZGlmeQAyMDE5LTA2LTA0VDIzOjQ1OjMyKzA4OjAwOlLMpwAAACh0RVh0aWNjOmNvcHlyaWdodABDb3B5cmlnaHQgQXBwbGUgSW5jLiwgMjAxOC9MBUEAAAAXdEVYdGljYzpkZXNjcmlwdGlvbgBEaXNwbGF5FxuVuAAAAABJRU5ErkJggg=="
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此案例中定义了三层神经网络，包含了两个隐藏层。通过隐藏层将输入层转化为一个１０*1的向量。然后通过softmax 函数将此向量转化为概率函数，对应与每个分类的概率。通过线性变换将高维向量转华为类别数相同的向量，每个分量对应与每个类别。,然后通过soft函数模型![20180816101345151.png](attachment:20180816101345151.png)，将其转化为概率函数。最后通过最小化交叉熵损失函数的均值。\n",
    "![%EF%BC%92%EF%BC%92%EF%BC%92.png](attachment:%EF%BC%92%EF%BC%92%EF%BC%92.png)"
   ]
  },
  {
   "attachments": {
    "%EF%BC%93%EF%BC%93%EF%BC%93%EF%BC%93.png": {
     "image/png": 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"
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 梯度下降：\n",
    "　损失函数关于W和b的偏导数，在m个样本的情况下，可以写成所有样本点偏导数的平均形式：\n",
    " ![%EF%BC%93%EF%BC%93%EF%BC%93%EF%BC%93.png](attachment:%EF%BC%93%EF%BC%93%EF%BC%93%EF%BC%93.png)\n",
    " 接下来，和单个样本一样，更新w1,w2,和b来进行下一次的训练：\n",
    " ![%EF%BC%94%EF%BC%94%EF%BC%94.png](attachment:%EF%BC%94%EF%BC%94%EF%BC%94.png)\n",
    " 其中α是学习率，通过调整α的大小来调整梯度下降速率，过大会导致模型最最终结果周围震荡，会浪费更多时间，甚至达不到最终的最优解。当设置过小时，虽能走向最优解，但运行时间会随之增加。"
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算图\n",
    "　计算图是Tensorflow中最基本的一个概念，Tensorflow中的所有计算都会被转化为计算图上的节点。\n",
    " Tensorflow中的两个最基本的概念：Tensor 和，　Flow\n",
    "     Tensor:即张量，可以理解为多维数组\n",
    "     Flow:形象的表达了张量之间通过计算相互转化的过程\n",
    "     \n",
    " Tensor表明了TensorFlow中的数据结构，而Flow则体现了它的计算模型\n",
    " \n",
    " 计算图：（Tensorflow是通过计算图的形式来表述计算的编程系统。\n",
    "     基于Tensorflow这个编程系统中的每一个计算都是计算图的上的一个节点，而节点与节点之间的连线则代表计算之间的依赖关系。\n",
    "     如![%EF%BC%94%EF%BC%94%EF%BC%94%EF%BC%94%EF%BC%94%EF%BC%94.jpg](attachment:%EF%BC%94%EF%BC%94%EF%BC%94%EF%BC%94%EF%BC%94%EF%BC%94.jpg)\n",
    "   上图中的a, b,add三个节点都代表一个计算，而两条带有箭头的连线则代表三个计算之间的依赖关系。如果一个计算的输入依赖于另一个计算的输出，即代表两个计算之间的依赖关系。在上图中a,b两个计算不依赖其他任何计算的输出，add的计算依赖a,b两个计算的数车，所以我们可以看到分别从a,b到add的两条连线，因为没有任何计算依赖add的结果，所以从add的节点没有任何连线指向其他节点。\n",
    "   在用Tensorflow的过程中，有两个阶段：\n",
    "   阶段１：定义计算图中的所有计算\n",
    "   阶段２：执行计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "模型效果较差的原因：\n",
    "    1.模型选用的层数只有两层，可以通过增加层数来达到一个较好的效果\n",
    "    2.学习率设置过大，也可能是模型训练效果过差的原因\n",
    "    3.训练的轮数不够，增家训练的轮数也可以增加训练效果\n",
    "    "
   ]
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